
Some of the very popular generative models in Deep Learning (DL) are VAEs [1]} and GANs [2]}. VAEs tend to maximize the likelihood of the generated data coming from the actual distribution while assuming a Gaussian prior. Different VAE architectures have performed well in generating various types of images ranging from handwritten digits [1]}, [4]} to faces [1]} to house numbers [4]} and to CIFAR images [4]}. On the other hand, in a GAN, the generator and discriminator play a minmax game until they reach an equilibrium. At this point, the distribution of generator is close to that of the original data. GANs are known to have been used for simple image generation in [8]} as well as to more sophisticated tasks such as style-transfer [9]}, [10]}, super-resolution [11]} image-to-image translation [12]}, [13]} and removing image in-painting [14]}. All of the aforesaid works are limited to only raster images.
